


import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :					Any      =       logging.get_logger(__name__)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							SwinConfig.from_pretrained(
         '''microsoft/swin-tiny-patch4-window7-224'''				,out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
     snake_case											=							MaskFormerConfig(backbone_config=UpperCamelCase_							)

     snake_case											=							'''huggingface/label-files'''
     if "ade20k-full" in model_name:
          # this should be ok
          snake_case											=							8_47
          snake_case											=							'''maskformer-ade20k-full-id2label.json'''
     elif "ade" in model_name:
          # this should be ok
          snake_case											=							1_50
          snake_case											=							'''ade20k-id2label.json'''
     elif "coco-stuff" in model_name:
          # this should be ok
          snake_case											=							1_71
          snake_case											=							'''maskformer-coco-stuff-id2label.json'''
     elif "coco" in model_name:
          # TODO
          snake_case											=							1_33
          snake_case											=							'''coco-panoptic-id2label.json'''
     elif "cityscapes" in model_name:
          # this should be ok
          snake_case											=							19
          snake_case											=							'''cityscapes-id2label.json'''
     elif "vistas" in model_name:
          # this should be ok
          snake_case											=							65
          snake_case											=							'''mapillary-vistas-id2label.json'''

     snake_case											=							json.load(open(hf_hub_download(UpperCamelCase_				,UpperCamelCase_				,repo_type='''dataset'''							)				,'''r'''							)							)
     snake_case											=							{int(UpperCamelCase_							): v for k, v in idalabel.items()}

     return config
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							[]
     # stem
     # fmt: off
     rename_keys.append(('''backbone.patch_embed.proj.weight''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight''')							)
     rename_keys.append(('''backbone.patch_embed.proj.bias''', '''model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias''')							)
     rename_keys.append(('''backbone.patch_embed.norm.weight''', '''model.pixel_level_module.encoder.model.embeddings.norm.weight''')							)
     rename_keys.append(('''backbone.patch_embed.norm.bias''', '''model.pixel_level_module.encoder.model.embeddings.norm.bias''')							)
     # stages
     for i in range(len(config.backbone_config.depths							)							):
          for j in range(config.backbone_config.depths[i]							):
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''')							)
               rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''')							)

          if i < 3:
               rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''')							)
               rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''')							)
               rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''')							)
          rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''')							)
          rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''')							)

     # FPN
     rename_keys.append(('''sem_seg_head.layer_4.weight''', '''model.pixel_level_module.decoder.fpn.stem.0.weight''')							)
     rename_keys.append(('''sem_seg_head.layer_4.norm.weight''', '''model.pixel_level_module.decoder.fpn.stem.1.weight''')							)
     rename_keys.append(('''sem_seg_head.layer_4.norm.bias''', '''model.pixel_level_module.decoder.fpn.stem.1.bias''')							)
     for source_index, target_index in zip(range(3				,0				,-1							)				,range(0				,3							)							):
          rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''')							)
          rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''')							)
          rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''')							)
          rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''')							)
          rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''')							)
          rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''')							)
     rename_keys.append(('''sem_seg_head.mask_features.weight''', '''model.pixel_level_module.decoder.mask_projection.weight''')							)
     rename_keys.append(('''sem_seg_head.mask_features.bias''', '''model.pixel_level_module.decoder.mask_projection.bias''')							)

     # Transformer decoder
     for idx in range(config.decoder_config.decoder_layers							):
          # self-attention out projection
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''')							)
          # cross-attention out projection
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''')							)
          # MLP 1
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''')							)
          # MLP 2
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''')							)
          # layernorm 1 (self-attention layernorm)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''')							)
          # layernorm 2 (cross-attention layernorm)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''')							)
          # layernorm 3 (final layernorm)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''')							)

     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.weight''', '''model.transformer_module.decoder.layernorm.weight''')							)
     rename_keys.append(('''sem_seg_head.predictor.transformer.decoder.norm.bias''', '''model.transformer_module.decoder.layernorm.bias''')							)

     # heads on top
     rename_keys.append(('''sem_seg_head.predictor.query_embed.weight''', '''model.transformer_module.queries_embedder.weight''')							)

     rename_keys.append(('''sem_seg_head.predictor.input_proj.weight''', '''model.transformer_module.input_projection.weight''')							)
     rename_keys.append(('''sem_seg_head.predictor.input_proj.bias''', '''model.transformer_module.input_projection.bias''')							)

     rename_keys.append(('''sem_seg_head.predictor.class_embed.weight''', '''class_predictor.weight''')							)
     rename_keys.append(('''sem_seg_head.predictor.class_embed.bias''', '''class_predictor.bias''')							)

     for i in range(3							):
          rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''')							)
          rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''')							)
     # fmt: on

     return rename_keys
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							dct.pop(UpperCamelCase_							)
     snake_case											=							val
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							[int(backbone_config.embed_dim * 2**i							) for i in range(len(backbone_config.depths							)							)]
     for i in range(len(backbone_config.depths							)							):
          snake_case											=							num_features[i]
          for j in range(backbone_config.depths[i]							):
               # fmt: off
               # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
               snake_case											=							state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''							)
               snake_case											=							state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias'''							)
               # next, add query, keys and values (in that order) to the state dict
               snake_case											=							in_proj_weight[:dim, :]
               snake_case											=							in_proj_bias[: dim]
               snake_case											=							in_proj_weight[
                   dim : dim * 2, :
               ]
               snake_case											=							in_proj_bias[
                   dim : dim * 2
               ]
               snake_case											=							in_proj_weight[
                   -dim :, :
               ]
               snake_case											=							in_proj_bias[-dim :]
               # fmt: on
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							config.decoder_config.hidden_size
     for idx in range(config.decoder_config.decoder_layers							):
          # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
          snake_case											=							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''							)
          snake_case											=							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias'''							)
          # next, add query, keys and values (in that order) to the state dict
          snake_case											=							in_proj_weight[: hidden_size, :]
          snake_case											=							in_proj_bias[:config.hidden_size]
          snake_case											=							in_proj_weight[hidden_size : hidden_size * 2, :]
          snake_case											=							in_proj_bias[hidden_size : hidden_size * 2]
          snake_case											=							in_proj_weight[-hidden_size :, :]
          snake_case											=							in_proj_bias[-hidden_size :]
          # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
          snake_case											=							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''							)
          snake_case											=							state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias'''							)
          # next, add query, keys and values (in that order) to the state dict
          snake_case											=							in_proj_weight[: hidden_size, :]
          snake_case											=							in_proj_bias[:config.hidden_size]
          snake_case											=							in_proj_weight[hidden_size : hidden_size * 2, :]
          snake_case											=							in_proj_bias[hidden_size : hidden_size * 2]
          snake_case											=							in_proj_weight[-hidden_size :, :]
          snake_case											=							in_proj_bias[-hidden_size :]
     # fmt: on
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     snake_case											=							Image.open(requests.get(UpperCamelCase_				,stream=UpperCamelCase_							).raw							)
     return im
@torch.no_grad()
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_ = False							):



     """simple docstring"""






     snake_case											=							get_maskformer_config(UpperCamelCase_							)

     # load original state_dict
     with open(UpperCamelCase_				,'''rb'''							) as f:
          snake_case											=							pickle.load(UpperCamelCase_							)
     snake_case											=							data['''model''']

     # for name, param in state_dict.items():
     #     print(name, param.shape)

     # rename keys
     snake_case											=							create_rename_keys(UpperCamelCase_							)
     for src, dest in rename_keys:
          rename_key(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							)
     read_in_swin_q_k_v(UpperCamelCase_				,config.backbone_config							)
     read_in_decoder_q_k_v(UpperCamelCase_				,UpperCamelCase_							)

     # update to torch tensors
     for key, value in state_dict.items():
          snake_case											=							torch.from_numpy(UpperCamelCase_							)

     # load 🤗 model
     snake_case											=							MaskFormerForInstanceSegmentation(UpperCamelCase_							)
     model.eval()

     for name, param in model.named_parameters():
          print(UpperCamelCase_				,param.shape							)

     snake_case					,    snake_case											=							model.load_state_dict(UpperCamelCase_				,strict=UpperCamelCase_							)
     assert missing_keys == [
         "model.pixel_level_module.encoder.model.layernorm.weight",
         "model.pixel_level_module.encoder.model.layernorm.bias",
     ]
     assert len(UpperCamelCase_							) == 0, F'''Unexpected keys: {unexpected_keys}'''

     # verify results
     snake_case											=							prepare_img()
     if "vistas" in model_name:
          snake_case											=							65
     elif "cityscapes" in model_name:
          snake_case											=							6_55_35
     else:
          snake_case											=							2_55
     snake_case											=							True if '''ade''' in model_name else False
     snake_case											=							MaskFormerImageProcessor(ignore_index=UpperCamelCase_				,reduce_labels=UpperCamelCase_							)

     snake_case											=							image_processor(UpperCamelCase_				,return_tensors='''pt'''							)

     snake_case											=							model(**UpperCamelCase_							)

     print('''Logits:'''				,outputs.class_queries_logits[0, :3, :3]							)

     if model_name == "maskformer-swin-tiny-ade":
          snake_case											=							torch.tensor(
              [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]							)
     assert torch.allclose(outputs.class_queries_logits[0, :3, :3]				,UpperCamelCase_				,atol=1e-4							)
     print('''Looks ok!'''							)

     if pytorch_dump_folder_path is not None:
          print(F'''Saving model and image processor to {pytorch_dump_folder_path}'''							)
          Path(UpperCamelCase_							).mkdir(exist_ok=UpperCamelCase_							)
          model.save_pretrained(UpperCamelCase_							)
          image_processor.save_pretrained(UpperCamelCase_							)

     if push_to_hub:
          print('''Pushing model and image processor to the hub...'''							)
          model.push_to_hub(F'''nielsr/{model_name}'''							)
          image_processor.push_to_hub(F'''nielsr/{model_name}'''							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Union[str, Any]      =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="maskformer-swin-tiny-ade",
     type=str,
     help=("Name of the MaskFormer model you'd like to convert",),
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl",
     type=str,
     help="Path to the original state dict (.pth file).",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 _SCREAMING_SNAKE_CASE     :					Union[str, Any]      =       parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )








import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


_SCREAMING_SNAKE_CASE     :					List[str]      =       get_tests_dir("fixtures/test_sentencepiece.model")

if is_torch_available():
 from transformers.models.mbart.modeling_mbart import shift_tokens_right

_SCREAMING_SNAKE_CASE     :					Any      =       25_00_04
_SCREAMING_SNAKE_CASE     :					Any      =       25_00_20

@require_sentencepiece
@require_tokenizers
class     A__   (       snake_case__			,   unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						MBartaaTokenizer
    __magic_name__         =						MBartaaTokenizerFast
    __magic_name__         =						True
    __magic_name__         =						True
    def 						a_     (			self       ):
         super().setUp()

         # We have a SentencePiece fixture for testing
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )
         tokenizer.save_pretrained(self.tmpdirname       )
    def 						a_     (			self       ):
         snake_case											=							'''<s>'''
         snake_case											=							0

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case       )     ,   __snake_case       )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							list(self.get_tokenizer().get_vocab().keys()       )

         self.assertEqual(vocab_keys[0]     ,   '''<s>'''       )
         self.assertEqual(vocab_keys[1]     ,   '''<pad>'''       )
         self.assertEqual(vocab_keys[-1]     ,   '''<mask>'''       )
         self.assertEqual(len(__snake_case       )     ,   1_0_5_4       )
    def 						a_     (			self       ):
         self.assertEqual(self.get_tokenizer().vocab_size     ,   1_0_5_4       )
    def 						a_     (			self       ):
         snake_case											=							MBartaaTokenizer(__snake_case     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''     ,   keep_accents=__snake_case       )

         snake_case											=							tokenizer.tokenize('''This is a test'''       )
         self.assertListEqual(__snake_case     ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']       )

         self.assertListEqual(
             tokenizer.convert_tokens_to_ids(__snake_case       )     ,   [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]     ,   )

         snake_case											=							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''9''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''é''', '''.''']     ,   )
         snake_case											=							tokenizer.convert_tokens_to_ids(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [
                 value + tokenizer.fairseq_offset
                 for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
             ]     ,   )

         snake_case											=							tokenizer.convert_ids_to_tokens(__snake_case       )
         self.assertListEqual(
             __snake_case     ,   [SPIECE_UNDERLINE + '''I''', SPIECE_UNDERLINE + '''was''', SPIECE_UNDERLINE + '''b''', '''or''', '''n''', SPIECE_UNDERLINE + '''in''', SPIECE_UNDERLINE + '''''', '''<unk>''', '''2''', '''0''', '''0''', '''0''', ''',''', SPIECE_UNDERLINE + '''and''', SPIECE_UNDERLINE + '''this''', SPIECE_UNDERLINE + '''is''', SPIECE_UNDERLINE + '''f''', '''al''', '''s''', '''<unk>''', '''.''']     ,   )
    @slow
    def 						a_     (			self       ):
         # fmt: off
         snake_case											=							{'''input_ids''': [[2_5_0_0_0_4, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 5_4_2_8, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 1_2_3_9_9, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 2_7_8_9, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 8_0_5, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [2_5_0_0_0_4, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [2_5_0_0_0_4, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=__snake_case     ,   model_name='''facebook/mbart-large-50'''     ,   revision='''d3913889c59cd5c9e456b269c376325eabad57e2'''     ,   )




    def 						a_     (			self       ):
         if not self.test_slow_tokenizer:
              # as we don't have a slow version, we can't compare the outputs between slow and fast versions
              return

         snake_case											=							(self.rust_tokenizer_class, '''hf-internal-testing/tiny-random-mbart50''', {})
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                   snake_case											=							self.rust_tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )
                   snake_case											=							self.tokenizer_class.from_pretrained(__snake_case     ,   **__snake_case       )

                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files + the tokenizer.json file for the fast one
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )
                   snake_case											=							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f       )
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )
                        # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                        # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=True
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it save with the same files
                   self.assertSequenceEqual(__snake_case     ,   __snake_case       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )

                   # Save tokenizer rust, legacy_format=False
                   snake_case											=							tempfile.mkdtemp()

                   snake_case											=							tokenizer_r.save_pretrained(__snake_case     ,   legacy_format=__snake_case       )
                   snake_case											=							tokenizer_p.save_pretrained(__snake_case       )

                   # Checks it saved the tokenizer.json file
                   self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files       )       )

                   # Checks everything loads correctly in the same way
                   snake_case											=							tokenizer_r.from_pretrained(__snake_case       )
                   snake_case											=							tokenizer_p.from_pretrained(__snake_case       )

                   # Check special tokens are set accordingly on Rust and Python
                   for key in tokenizer_pp.special_tokens_map:
                        self.assertTrue(hasattr(__snake_case     ,   __snake_case       )       )

                   shutil.rmtree(__snake_case       )






@require_torch
@require_sentencepiece
@require_tokenizers
class     A__   (       unittest.TestCase      ):







    """simple docstring"""

    __magic_name__         =						'facebook/mbart-large-50-one-to-many-mmt'
    __magic_name__         =						[
        ' UN Chief Says There Is No Military Solution in Syria',
        ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
    ]
    __magic_name__         =						[
        'Şeful ONU declară că nu există o soluţie militară în Siria',
        'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
        ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
        ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
    ]
    __magic_name__         =						[EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
    @classmethod
    def 						a_     (			cls       ):
         snake_case											=							MBartaaTokenizer.from_pretrained(
             cls.checkpoint_name     ,   src_lang='''en_XX'''     ,   tgt_lang='''ro_RO'''       )
         snake_case											=							1
         return cls
    def 						a_     (			self       ):
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']     ,   2_5_0_0_0_1       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']     ,   2_5_0_0_0_4       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']     ,   2_5_0_0_2_0       )
         self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''mr_IN''']     ,   2_5_0_0_3_8       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer.batch_encode_plus(self.src_text       ).input_ids[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertIn(__snake_case     ,   self.tokenizer.all_special_ids       )
         snake_case											=							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
         snake_case											=							self.tokenizer.decode(__snake_case     ,   skip_special_tokens=__snake_case       )
         snake_case											=							self.tokenizer.decode(generated_ids[1:]     ,   skip_special_tokens=__snake_case       )
         self.assertEqual(__snake_case     ,   __snake_case       )
         self.assertNotIn(self.tokenizer.eos_token     ,   __snake_case       )
    def 						a_     (			self       ):
         snake_case											=							['''this is gunna be a long sentence ''' * 2_0]
         assert isinstance(src_text[0]     ,   __snake_case       )
         snake_case											=							1_0
         snake_case											=							self.tokenizer(__snake_case     ,   max_length=__snake_case     ,   truncation=__snake_case       ).input_ids[0]
         self.assertEqual(ids[0]     ,   __snake_case       )
         self.assertEqual(ids[-1]     ,   2       )
         self.assertEqual(len(__snake_case       )     ,   __snake_case       )
    def 						a_     (			self       ):
         self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']       )     ,   [2_5_0_0_5_3, 2_5_0_0_0_1]       )
    def 						a_     (			self       ):
         snake_case											=							tempfile.mkdtemp()
         snake_case											=							self.tokenizer.fairseq_tokens_to_ids
         self.tokenizer.save_pretrained(__snake_case       )
         snake_case											=							MBartaaTokenizer.from_pretrained(__snake_case       )
         self.assertDictEqual(new_tok.fairseq_tokens_to_ids     ,   __snake_case       )
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   return_tensors='''pt'''       )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
         assert batch.input_ids[1][0] == EN_CODE
         assert batch.input_ids[1][-1] == 2
         assert batch.labels[1][0] == RO_CODE
         assert batch.labels[1][-1] == 2
         assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(
             self.src_text     ,   text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=len(self.expected_src_tokens       )     ,   return_tensors='''pt'''     ,   )
         snake_case											=							shift_tokens_right(batch['''labels''']     ,   self.tokenizer.pad_token_id       )

         self.assertIsInstance(__snake_case     ,   __snake_case       )

         self.assertEqual((2, 1_4)     ,   batch.input_ids.shape       )
         self.assertEqual((2, 1_4)     ,   batch.attention_mask.shape       )
         snake_case											=							batch.input_ids.tolist()[0]
         self.assertListEqual(self.expected_src_tokens     ,   __snake_case       )
         self.assertEqual(2     ,   batch.decoder_input_ids[0, 0]       )  # decoder_start_token_id
         # Test that special tokens are reset
         self.assertEqual(self.tokenizer.prefix_tokens     ,   [EN_CODE]       )
         self.assertEqual(self.tokenizer.suffix_tokens     ,   [self.tokenizer.eos_token_id]       )
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer(self.src_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=3     ,   return_tensors='''pt'''       )
         snake_case											=							self.tokenizer(
             text_target=self.tgt_text     ,   padding=__snake_case     ,   truncation=__snake_case     ,   max_length=1_0     ,   return_tensors='''pt'''       )
         snake_case											=							targets['''input_ids''']
         snake_case											=							shift_tokens_right(__snake_case     ,   self.tokenizer.pad_token_id       )

         self.assertEqual(batch.input_ids.shape[1]     ,   3       )
         self.assertEqual(batch.decoder_input_ids.shape[1]     ,   1_0       )




    @require_torch
    def 						a_     (			self       ):
         snake_case											=							self.tokenizer._build_translation_inputs(
             '''A test'''     ,   return_tensors='''pt'''     ,   src_lang='''en_XX'''     ,   tgt_lang='''ar_AR'''       )

         self.assertEqual(
             nested_simplify(__snake_case       )     ,   {
                 # en_XX, A, test, EOS
                 '''input_ids''': [[2_5_0_0_0_4, 6_2, 3_0_3_4, 2]],
                 '''attention_mask''': [[1, 1, 1, 1]],
                 # ar_AR
                 '''forced_bos_token_id''': 2_5_0_0_0_1,
             }     ,   )




